The ability to ionize gases using ion mobility systems is useful for a wide range of applications including many chemical detection applications. Ionization techniques, in which a gas sample is ionized and then separated into constituent parts that can be detected individually, are widely used for gas composition sensing. Two well-known examples are Ion Mobility Spectrometry (IMS) and Field Asymmetric Ion Mobility Spectrometry (FAIMS), also known as Differential Mobility Spectrometry (DMS). Ion mobility detection techniques tend to be very well suited to measuring trace constituents of gas mixtures that often consist of a carrier gas with additional gases mixed in at low concentrations (for example part-per-million or part-per-billion levels). Ion mobility techniques can also be used effectively over a range of gas pressures, including pressures close to one atmosphere. This makes them useful for, amongst other things, measuring low-level impurities in air. Because they work by measuring properties of ionized molecules and because gas samples for analysis generally consist mainly of neutral molecules, ion-mobility-based detectors generally incorporate an ionizer. The sample gas is passed through the ionizer to produce a population of ionized molecules that are then manipulated in some way involving separation or selection of ionized molecules according to their behavior in an electric field, before being detected. Ionizers commonly in use include radioactive sources, light-based devices such as ultra-violet lamps, and electrostatic devices such as corona discharge ionizers.
Practical chemical detectors such as Field Asymmetric Ion Mobility Spectrometry (FAIMS) systems must compensate for varying environmental conditions as these can affect the output of the spectrometer. There are also a number of implementation challenges that the designer must overcome in order to produce a repeatable and reliable detection system. As one skilled in the art would recognize, a wide variety of non-idealities are encountered in practice. For instance, some of those most relevant to FAIMS systems fall into the following categories:                Environmental and system variations and non-uniformities (e.g. changes in temperature, pressure, humidity and pump rate);        Non-ideal device physics (e.g. charge mirroring); and        Operational constraints (e.g. must provide certain levels of efficiency and stability while maintaining performance).        
In this regard, it is to be understood and appreciated changes in ambient conditions such as temperature, pressure, and ambient humidity often lead to changes in FAIMS spectra. It is noted theoretical and/or empirical corrections exist for these effects. While theoretical approaches work well for pressure and, to a lesser extent, temperature, not all environmental conditions can easily be corrected in this manner. Empirical corrections can be determined for all ambient variables, but this approach necessitates prior characterization of analytes across a multi-dimensional matrix of conditions and the storage of this data for each analyte, which can be a time consuming and laborious process. It is thus desirable to provide a system or approach that enables conditions at the sensor head to be controlled in such a system such that each analyte only has to be characterized under a small set of environmental conditions.
In regards to a FAIMS system, since it is a highly sensitive system, FAIMS spectrometers can often detect down to parts-per-billion levels or more. Direct sampling from the ambient environment can thus be problematic for such highly sensitive systems, as they can be saturated by higher concentrations of analytes or interferents, thus hiding the analytes of actual interest. High concentrations of some analytes can persist within the system as contamination due to absorption onto internal surfaces, adversely affecting performance.
It is advantageous to determine how the system behaves under known conditions. When sampling from the ambient environment there are always unknown elements of the spectra, which can make base-lining system performance troublesome.
It is noted a standalone FAIMS system typically requires a pump component to drive a flow of air through the FAIMS system. However, pumps introduce pulsatility into the flow, which can distort FAIMS spectra (for example, by altering peak positions and splitting peaks). Furthermore, the flow generated by pumps as they age, warm-up, etc. can vary, which may cause significant errors in the flow through the FAIMS system.
Further, when there is the presence of multiple species in a FAIMS system, the finite amount of charge available is typically distributed between the species according to their concentrations and charge affinities. This makes its difficult to quantitatively judge the concentration of the species present, particularly if one of these species is present in high concentrations or charge affinities.